Lower thresholds will further reduce variance at the cost of introducing more bias and shifting the target population or estimand definition further [1]}, [2]}. The ideal truncation level will vary on a case-by-case basis and can be set empirically, e.g. by progressively truncating the weights [1]}, [4]}. Density plots are likely helpful to assess the dispersion of the weights and identify an optimal cutoff point. Weight truncation is likely of little utility where there is sufficient overlap and the weights are well-behaved.
